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Methodology
EveryLab's comprehensive evaluation framework for Asia-first AI assessment
Overview
EveryLab's evaluation methodology is built on the foundation that AI systems must be evaluated within the cultural, linguistic, and social contexts where they will be deployed. Unlike traditional benchmarks that apply Western-centric evaluation criteria, our framework incorporates Asia-specific nuances that reflect real-world usage patterns across diverse Asian markets.
Our approach combines automated metrics with expert human evaluation from regional specialists, ensuring that models are assessed for both technical capability and cultural appropriateness. This methodology addresses the critical gap in current AI evaluation: the lack of culturally-aware assessment frameworks for non-Western contexts.
Core Principles
Cultural Context First
Evaluations prioritize cultural appropriateness and regional communication norms
Expert-Driven Assessment
Regional experts with deep domain knowledge conduct human evaluations
Private Dataset Protection
Proprietary evaluation sets prevent overfitting and ensure authentic assessment
Multi-Dimensional Analysis
Comprehensive evaluation across capability, safety, and cultural dimensions
Evaluation Framework
Five Axes of LLM Capability Assessment
EveryLab evaluates models across five fundamental dimensions, each tailored to Asia-specific contexts and requirements:
1. Instruction Following
How well the model understands culturally-specific instructions, including indirect communication patterns common in Asian contexts and hierarchical language structures.
2. Cultural Creativity
The model's ability to generate culturally appropriate creative content that resonates with local audiences while respecting traditional and contemporary cultural values.
3. Responsibility & Safety
Adherence to region-specific safety constraints including taboo topics, religious sensitivities, and social norms that vary significantly across Asian markets.
4. Contextual Reasoning
Logical reasoning capability within cultural frameworks, including understanding of social hierarchies, business etiquette, and decision-making patterns specific to Asian contexts.
5. Cultural Factuality
Accuracy of information regarding regional facts, historical context, business practices, and current events with sensitivity to cultural perspectives and interpretations.
Capability Assessment
Helpfulness and functional performance evaluation:
- • Cross-cultural communication effectiveness
- • Language-specific reasoning and logic
- • Domain expertise in Asian business contexts
- • Technical accuracy with regional variations
- • User experience and interaction quality
Safety Assessment
Harmlessness and cultural sensitivity evaluation:
- • Cultural taboo and sensitive topic handling
- • Religious and spiritual content appropriateness
- • Social bias and stereotype avoidance
- • Privacy and data protection compliance
- • Misinformation and fact-checking accuracy
Three-Tier Evaluation Process
1. Model Evaluation
Comprehensive assessment through automated and human evaluation combining technical performance with cultural appropriateness:
Version Control
Regression testing aligned with model deployment schedules, comparing versions for cultural sensitivity degradation and capability improvements.
Exploratory Assessment
Expert evaluation at major checkpoints using embedding maps to identify strengths and weaknesses across cultural and linguistic dimensions.
Model Certification
Standardized testing battery ensuring minimum performance standards for deployment in specific Asian markets and use cases.
2. Continuous Model Monitoring
Real-time performance tracking in production environments with automated anomaly detection and expert escalation:
- Automated review of rolling response samples for cultural appropriateness drift
- Anomaly detection for responses outside established cultural boundaries
- Expert human reviewer escalation for problematic content identification
- Integration with reliability monitoring for comprehensive service health assessment
3. Expert Red Teaming
Iterative adversarial testing by regional experts to identify cultural vulnerabilities and safety risks:
Risk Categories
Expert Qualification Process
Red team experts undergo rigorous vetting including cultural competency assessment, domain-specific interviews, and completion of golden tasks with known answers. All experts are native speakers with demonstrated expertise in their evaluation domains.
Dataset Integrity & Evaluation Quality
Contamination Prevention
Unlike public benchmarks, EveryLab's proprietary datasets remain strictly private and unpublished, ensuring they cannot be exploited or incorporated into model training data. We address key evaluation challenges:
| Evaluation Challenge | EveryLab Solution |
|---|---|
| Contamination & Overfitting | Private Unexploitable Datasets |
| Inconsistent Reporting | Transparent & Consistent Methodologies |
| Unverified Expertise | Vetted Regional Domain Experts |
| Cultural Bias | Asia-First Evaluation Framework |
| Inadequate Tooling | EveryLab Evaluation Platform |
Quality Assurance Process
Data Quality
- • Multi-round prompt and rating reviews
- • Internal QA validation processes
- • Expert consensus verification
- • Cultural appropriateness validation
- • Inter-annotator agreement tracking (κ > 0.75)
Evaluation Integrity
- • One-time model evaluation per dataset
- • Limited API exposure risk management
- • Trusted third-party collaboration
- • Regular methodology audits
- • Transparent result reporting
Helpfulness vs. Harmlessness Balance
Model optimization involves inherent tradeoffs between helpfulness and harmlessness. EveryLab's approach recognizes that optimal balance points vary significantly across Asian cultural contexts and use cases:
Conservative Markets
Higher emphasis on harmlessness for markets with strict social norms and regulatory requirements
Business Applications
Balanced approach optimizing for professional effectiveness while maintaining cultural sensitivity
Educational Use
Prioritized safety with culturally-appropriate educational content delivery
Regional Expert Network
EveryLab maintains a diverse network of verified regional experts across Asia, each bringing deep cultural knowledge and domain expertise to our evaluation process. Our expert qualification and management system ensures consistent, high-quality assessments while maintaining cultural authenticity.
Commitment to Transparency
EveryLab publishes detailed evaluation methodologies and key insights beyond raw rankings. We welcome community feedback to refine our approaches and maintain accountability through collaboration with trusted third-party organizations. Our goal is to advance the field of culturally-aware AI evaluation while maintaining the highest standards of integrity and transparency.